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Learn Time Series Analysis with Python

person icon Srikanth Guskra

4.2

Learn Time Series Analysis with Python

Experience project-based learning to master Python, Time Series Model Additive, Multiplicative, AR, Moving Average, Exponential, and ARIMA models.

updated on icon Updated on May, 2024

language icon Language - English

person icon Srikanth Guskra

English [CC]

category icon Data Analysis,IT & Software,Python

Lectures -75

Resources -1

Duration -7.5 hours

4.2

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Course Description

Time Series Analysis with Python course will help you learn to work with a number of Python libraries, providing you with complete training. You will use the powerful time-series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, statsmodels, Sklearn, and ARCH.

Time Series Analysis with Python Overview

The course starts with programming in Python which is the essential skill required and then we will explore the fundamental time series theory to help you understand the modeling that comes afterward. 

Time series analysis and forecasting is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. 

This course covers all types of modeling techniques for forecasting and analysis. With these tools, we will master the most widely used models out there:

  • Additive Model

  • Multiplicative Model

  • AR (autoregressive model)

  • Simple Moving Average

  • Weighted Moving Average

  • Exponential Moving Average

  • ARMA (autoregressive-moving-average model)

  • ARIMA (autoregressive integrated moving average model)

  • Auto ARIMA

Goals

What will you learn in this course:

  • Master basic to advanced Time Series methods.

  • Learn auto-regressive methods,

  • Learn Time Series Visualization in Python.

  • ARMA, ARIMA, SARIMA in Python.

  • ACF and PACF.

  • Auto ARIMA in Python.

  • Additive Model

  • Multiplicative Model

  • AR (autoregressive model)

  • Simple Moving Average

  • Weighted Moving Average

  • Exponential Moving Average

Prerequisites

What are the prerequisites for this course?

  • Basic knowledge of Statistics.

  • Basic understanding of Python.

  • Should have a Gmail Account and should be able to open Google Drive.

Learn Time Series Analysis with Python

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
4 Lectures
  • play icon Introduction 04:09 04:09
  • play icon What is time series data 02:38 02:38
  • play icon Components of Time Series 03:37 03:37
  • play icon Download Recourses
Setting Up Course
2 Lectures
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Time Series Visualization
8 Lectures
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Linear Regression Intuition
9 Lectures
Tutorialspoint
Time Series Forecasting with Linear Regression
5 Lectures
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Additive Time Series Model
6 Lectures
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Multiplicative Time Series Model
7 Lectures
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Auto Regressive Methods
8 Lectures
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Smoothing Methods (Moving Average)
10 Lectures
Tutorialspoint
Non Seasonal ARIMA models
13 Lectures
Tutorialspoint
Auto ARIMA
3 Lectures
Tutorialspoint

Instructor Details

Srikanth Guskra

Srikanth Guskra


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